Abstract
An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be performed in a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks AI introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence.
Originally published in Berkeley Technology Law Journal Commentaries (2020)
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Notes
- 1.
Louise Brett et al., AI and You: Perceptions of Artificial Intelligence from the EMEA financial services industry, Deloitte 9 (Apr. 2017), https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/technology/deloitte-cn-tech-ai-and-you-en-170801.pdf [https://perma.cc/R688-FSQS]
- 2.
Id. at 7, 12.
- 3.
See Algorithmic Trading Compliance in Wholesale Markets, Fin. Conduct Authority (Feb. 2018), https://www.fca.org.uk/publication/multi-firm-reviews/algorithmic-trading-compliance-wholesale-markets.pdf [https://perma.cc/WWS2-UERJ] [hereinafter Algorithmic Trading Compliance].
- 4.
See ECB guide to internal models, European Cent. Bank (Mar. 2018), https://www.bankingsupervision.europa.eu/legalframework/publiccons/pdf/internal_models/ssm.guidegeneraltopics.en.pdf [https://perma.cc/HV3T-HC6K]
- 5.
See Senior Managers Regime, Fin. Conduct Authority 3 (Mar. 2019), https://www.fca.org.uk/publication/corporate/applying-smr-to-fca.pdf [https://perma.cc/E95F-FPVE]
- 6.
See for example; How Is My Insurance Premium Calculated, Think Insurance,https://www.thinkinsurance.co.uk/personal/young-driver-insurance/how-is-my-insurance-premium-calculated
- 7.
Bank of England, What risks do banks take,https://www.bankofengland.co.uk/knowledgebank/what-risks-do-banks-take
- 8.
John Leonard, Admiral Insurance found to give higher quotes to Hotmail users and people called Mohammed, computing (Jan. 24, 2018), https://www.computing.co.uk/ctg/news/3025139/admiral-insurance-found-to-give-higher-quotes-to-hotmail-users-and-people-called-mohammed [https://perma.cc/7793-U9SX]
- 9.
James Rufus Koren, What does that Web search say about your credit?, L.A. Times (July 17, 2016), https://www.latimes.com/business/la-fi-zestfinance-baidu-20160715-snap-story.html [https://perma.cc/T2M3-WZ5M]
- 10.
Deborah B. Baum et al., Supreme Court Affirms FHA Disparate Impact Claims, Pillsbury Winthrop Shaw Pittman LLP (July 21, 2015), https://www.pillsburylaw.com/en/news-and-insights/supreme-court-affirms-fha-disparate-impact-claims.html [https://perma.cc/7J85-7AMP]
- 11.
Id.
- 12.
Tom Lowenthal, Essop v Home Office: Proving Indirect Discrimination, Oxford Hum. Rts. Hub (Apr. 6, 2017), http://ohrh.law.ox.ac.uk/essop-v-home-office-proving-indirect-discrimination [https://perma.cc/VN5K-Q6XP]
- 13.
Baum, supra note 8.
- 14.
Lowenthal, supra note 10.
- 15.
Mary Starks et al., Price discrimination in financial services, Fin. Conduct Authority 1 (July 2018), https://www.fca.org.uk/publication/research/price_discrimination_in_financial_services.pdf [https://perma.cc/3WK8-LT34]
- 16.
Id. at 6.
- 17.
Tom Bigham et al., AI and risk management, Deloitte 18 (2018), https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/deloitte-gx-ai-and-risk-management.pdf [https://perma.cc/D3BT-3VP5]
- 18.
See Algorithmic Trading Compliance, supra note 3.
- 19.
Id. at 8.
- 20.
Id. at 8–9.
- 21.
See Bigham et al., supra note 17.
- 22.
Id.
- 23.
See Algorithmic Trading Compliance, supra note 3 at 5, 16, 26.
- 24.
Id. at 5.
- 25.
Science and Technology Committee, Oral evidence: Algorithms in decision-making, HC 351, House of Commons (Jan. 23, 2018), http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/science-and-technology-committee/algorithms-in-%20decisionmaking/oral/77536.html [https://perma.cc/W4SY-WXYQ]
- 26.
Sandra Wachter et al., Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation, 7 Int’l Data Privacy L. 76, 76–99 (2017).
- 27.
Lester Holloway, Boycott car insurance firms that discriminate, Operation Black Vote (Jan. 25, 2018), https://www.obv.org.uk/news-blogs/boycott-car-insurance-firms-discriminate [https://perma.cc/9QWW-G7FN]
- 28.
Patrick Collinson, How an EU gender equality ruling widened inequality, Guardian (Jan. 14, 2017), https://www.theguardian.com/money/blog/2017/jan/14/eu-gender-ruling-car-insurance-inequality-worse [https://perma.cc/6BV8-334Z]
- 29.
Rebecca Rutt, How much does your job cost in car insurance, This is Money (Apr. 26, 2018), http://www.thisismoney.co.uk/money/bills/article-5637979/The-jobs-expensive-car-insurance.html [https://perma.cc/YE2F-PJV3]
- 30.
Nina Grgic-Hlaca et al., The case for process fairness in learning: Feature selection for fair decision making, NIPS Symp. on Machine Learning & L. (2016) [https://perma.cc/D5XT-FZJV]
- 31.
Pratik Gajane & Mykola Pechenizkiy, On formalizing fairness in prediction with machine learning, arXiv (May 28, 2018) https://arxiv.org/pdf/1710.03184.pdf [https://perma.cc/7TPK-HKFM]
- 32.
Id.
- 33.
Id.
- 34.
Matt Kusner et al., Counterfactual Fairness, arXiv (Mar. 8, 2018) https://arxiv.org/pdf/1703.06856.pdf [https://perma.cc/82PV-BF6Z]
- 35.
Starks et al., supra note 13.
- 36.
Gajane & Pechenizkiy, supra note 27.
- 37.
Grgic-Hlaca et al., supra note 26.
- 38.
Moritz Hardt et al., Equality of Opportunity in Supervised Learning, arXiv (Oct. 7, 2016), https://arxiv.org/pdf/1610.02413.pdf [https://perma.cc/3C7W-YESH]
- 39.
Gajane & Pechenizkiy, supra note 27.
- 40.
Algorithmic Trading Compliance, supra note 3.
- 41.
Starks et al., supra note 13.
- 42.
Bigham et al., supra note 15.
- 43.
Algorithmic Trading Compliance, supra note 3.
- 44.
TrueVoice, Deloitte UK, (2019), https://www2.deloitte.com/uk/en/pages/risk/solutions/truevoice.html [https://perma.cc/QEJ7-EKTV] (last visited Sept 18, 2019).
- 45.
Consumer Vulnerability, Fin. Conduct Authority (Feb. 2015), https://www.fca.org.uk/publication/occasional-papers/occasional-paper-8-exec-summary.pdf [https://perma.cc/GK77-WAVK]
- 46.
Id.
Bibliography
Baum, Deborah B., Julia E. Judish, David J. Stute, and John Scalia. Supreme court affirms FHA disparate impact claims. Pillsbury Winthrop Shaw Pittman LLP, July 21, 2015. https://www.pillsburylaw.com/en/news-and-insights/supreme-court-affirms-fha-disparate-impact-claims.html (last visited Jun 12, 2019).
Bigham, Tom, Valeria Gallo, Suchitra Nair, Michelle Seng Ah Lee, Sulabh Soral, Tom Mews, Alan Tua, and Morgane Fouche. 2018. AI and risk management. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/deloitte-gx-ai-and-risk-management.pdf (last visited Jun 12, 2019).
Brett, Louise, Patrick Laurent, Paolo Gianturco, and Tiago Pereira Durao. 2017. AI and You: Perceptions of Artificial Intelligence from the EMEA financial services industry. https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/technology/deloitte-cn-tech-ai-and-you-en-170801.pdf (last visited Jun 12, 2019).
Collinson, Patrick. How an EU gender equality ruling widened inequality. The Guardian, January 14, 2017. https://www.theguardian.com/money/blog/2017/jan/14/eu-gender-ruling-car-insurance-inequality-worse (last visited Jun 12, 2019).
Deloitte UK. 2019. TrueVoice.https://www2.deloitte.com/uk/en/pages/risk/solutions/truevoice.html (last visited Jun 12, 2019).
European Central Bank. 2018. ECB guide to internal models. https://www.bankingsupervision.europa.eu/legalframework/publiccons/pdf/internal_models/ssm.guidegeneraltopics.en.pdf (last visited Jun 12, 2019).
Financial Conduct Authority. 2015. Consumer vulnerability. https://www.fca.org.uk/publication/occasional-papers/occasional-paper-8-exec-summary.pdf (last visited Jun 12, 2019).
———. 2018a. Algorithmic trading compliance in wholesale markets. https://www.fca.org.uk/publication/multi-firm-reviews/algorithmic-trading-compliance-wholesale-markets.pdf (last visited Jun 12, 2019).
———. 2018b. Price discrimination in financial services. https://www.fca.org.uk/publication/research/price_discrimination_in_financial_services.pdf (last visited Jun 12, 2019).
———. 2019. Senior managers regime. https://www.fca.org.uk/publication/corporate/applying-smr-to-fca.pdf (last visited Jun 12, 2019).
Gajane, Pratik, and Mykola Pechenizkiy. 2017. On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184.
Grgic-Hlaca, Nina, Muhammad Bilal Zafar, Krishna Gummadi, and Adrian Weller. 2016. The case for process fairness in learning: Feature selection for fair decision making. In NIPS symposium on machine learning and the law.
Hardt, Moritz, Eric Price, and Nathan Srebro. 2016. Equality of opportunity in supervised learning. CoRR arXiv:1610.02413.
Holloway, Lester. Boycott car insurance firms that discriminate. Operation Black Vote, January 25, 2018. https://www.obv.org.uk/news-blogs/boycott-car-insurance-firms-discriminate (last visited Jun 12, 2019).
Koren, James Rufus. What does that Web search say about your credit. Los Angeles Times, July 17, 2016., https://www.latimes.com/business/la-fi-zestfinance-baidu-20160715-snap-story.html (last visited Jun 12, 2019).
Kusner, Matt, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual fairness. arXiv:1703.06856v2.
Leonard, John. 2018. Admiral Insurance found to give higher quotes to Hotmail users and people called Mohammed. https://www.computing.co.uk/ctg/news/3025139/admiral-insurance-found-to-give-higher-quotes-to-hotmail-users-and-people-called-mohammed (last visited Jun 12, 2019).
Lowenthal, Tom. Essop v home office: Proving indirect discrimination. Oxford Human Rights Hub, April 6, 2017. http://ohrh.law.ox.ac.uk/essop-v-home-office-proving-indirect-discrimination (last visited Jun 12, 2019).
Rutt, Rebecca. How much does your job cost in car insurance. This is Money, April 26, 2018. http://www.thisismoney.co.uk/money/bills/article-5637979/The-jobs-expensive-car-insurance.html (last visited Jun 12, 2019).
Science and Technology Committee. Oral evidence: Algorithms in decision-making, HC 351. January 23, 2018. http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/science-and-technology-committee/algorithms-in-%20decisionmaking/oral/77536.html (last visited Jun 12, 2019).
Wachter, Sandra, Brent Mittelstadt, and Luciano Floridi. 2017. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law 7: 76–99.
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Lee, M.S.A., Floridi, L., Denev, A. (2021). Innovating with Confidence: Embedding AI Governance and Fairness in a Financial Services Risk Management Framework. In: Floridi, L. (eds) Ethics, Governance, and Policies in Artificial Intelligence. Philosophical Studies Series, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-030-81907-1_20
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